Subspace fitting via sparse representation of signal covariance for DOA estimation

Abstract

Based on the orthogonality between the signal subspace and the noise subspace, we propose a sparse recovery method for the direction of arrival (DOA) estimation. With the assumption of uncorrelated sources, signal covariance matrix fitting is achieved by embedding the MUSIC-like weights into a quadratic minimization, which is capable of prompting the sparsity of the solution. Numerical results show that the proposed method outperforms some other sparse recovery methods.